---
license: mit
datasets:
- mazafard/portugues_ocr_dataset_full
language:
- en
- pt
base_model:
- microsoft/trocr-base-printed
pipeline_tag: image-to-text
emissions: 0.43
source: "AutoTrain" 
training_type: "fine-tuning" 
geographical_location: "US" 
hardware_used: "A100"
tags:
- ocr
- optical-character-recognition
- text-recognition
- trocr
- vision
- vision-and-language
model-index:
- name: trocr-finetuned-portugues
  results:
  - task:
      type: optical-character-recognition
      name: Optical Character Recognition
    dataset:
      type: mazafard/portugues_ocr_dataset_full
      name: portugues_ocr_dataset_full
      args: default
    metrics:
    - type: cer
      value: 0.01
      name: Character Error Rate
    - type: wer
      value: 0.05
      name: Word Error Rate
---
# Fine-tuned TrOCR Model for Portuguese

This repository contains a fine-tuned TrOCR model specifically trained for Optical Character Recognition (OCR) on **Portuguese** text. It's based on the [microsoft/trocr-base-printed](https://huggingface.co/microsoft/trocr-base-printed) model and has been further trained on a dataset of Portuguese text images.

## Model Description

The model is a VisionEncoderDecoderModel from the Hugging Face Transformers library. It combines a vision encoder (to process images) and a text decoder (to generate text) for OCR tasks. 

- **Base Model:** microsoft/trocr-base-printed
- **Fine-tuning Dataset:** mazafard/portugues_ocr_dataset_full
- **Language:** Portuguese

## Intended Use

This model is intended for extracting text from images containing Portuguese text. It can be used for various applications, such as:

- Digitizing Portuguese books and documents
- Automating data entry from Portuguese forms and invoices
- Extracting information from Portuguese screenshots or scanned images

## How to Use

**1. Install Dependencies:**
```python
bash pip install transformers datasets Pillow requests
```
**2. Load the Model and Processor:**
```python
python from transformers import VisionEncoderDecoderModel, TrOCRProcessor from PIL import Image

model = VisionEncoderDecoderModel.from_pretrained("mazafard/trocr-finetuned_20250422_125947") 
processor = TrOCRProcessor.from_pretrained("mazafard/trocr-finetuned_20250422_125947") 

image = Image.open("path/to/your/image.png").convert("RGB")

pixel_values = processor(image, return_tensors="pt").pixel_values

Generate prediction
generated_ids = model.generate(pixel_values) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

print(generated_text)
```

## Limitations

- The model may not perform well on handwritten text or text with unusual fonts or styles.
- It might make mistakes on complex layouts or images with low quality.

## Training Details

- **Dataset:** 
- **Training Parameters:**
```python
training_args = TrainingArguments(
    output_dir="./trocr-finetuned",
    per_device_train_batch_size=56,
    num_train_epochs=3,
    save_steps=500,
    logging_steps=50,
    learning_rate=5e-5,
    gradient_accumulation_steps=2,
    fp16=True,
    save_total_limit=2,
    remove_unused_columns=False,
     dataloader_num_workers=2,
)
```

## Evaluation


## Acknowledgements

- This model is based on the TrOCR model by Microsoft.


## License